agent-introspection-debugging
Skill1 starsChanged 3 months agoMITView on GitHub
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports.
We split this skill into the rules it gives an agent
14 rules. 10 are common, 0 are distinctive, and 4 are very specific to this project.
10
Common
Most of the 17 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
4
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Common: line 9
Use this skill when an agent run is failing repeatedly, consuming tokens without progress, looping on the same tools, or drifting away from …
- Common: line 29
Do not use this skill as the primary source for:
- Common: line 38
Before trying to recover, record the failure precisely.
- Common: line 84
stop repeated retries and restate the hypothesis
- Common: line 91
Do not claim unsupported auto-healing actions like “reset agent state” or “update harness config” unless you are actually doing them …
- Common: line 121
Prefer these interventions in order:
- Common: line 124
Verify the world state instead of trusting memory.
- Common: line 126
Run one discriminating check.
- Common: line 135
run one direct check
- Very specific: line 140
Use verification-loop after recovery if code was changed.
- Very specific: line 141
Use continuous-learning-v2 when the failure pattern is worth turning into an instinct or later skill.
- Very specific: line 142
Use council when the issue is not technical failure but decision ambiguity.
- Very specific: line 143
Use workspace-surface-audit if the failure came from conflicting local state or repo drift.
- Common: line 147
When this skill is active, do not end with “I fixed it” alone.
“Common” is measured: kept by at least 6 in 10 of the 17 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Agent Introspection Debugging
Use this skill when an agent run is failing repeatedly, consuming tokens without progress, looping on the same tools, or drifting away from the intended task.
This is a workflow skill, not a hidden runtime. It teaches the agent to debug itself systematically before escalating to a human.
When to Activate
- Maximum tool call / loop-limit failures
- Repeated retries with no forward progress
- Context growth or prompt drift that starts degrading output quality
- File-system or environment state mismatch between expectation and reality
- Tool failures that are likely recoverable with diagnosis and a smaller corrective action
Scope Boundaries
Activate this skill for:
- capturing failure state before retrying blindly
- diagnosing common agent-specific failure patterns
- applying contained recovery actions
- producing a structured human-readable debug report
Do not use this skill as the primary source for:
- feature verification after code changes; use
verification-loop - framework-specific debugging when a narrower ECC skill already exists
- runtime promises the current harness cannot enforce automatically
Four-Phase Loop
Phase 1: Failure Capture
Before trying to recover, record the failure precisely.
Capture:
- error type, message, and stack trace when available
- last meaningful tool call sequence
- what the agent was trying to do
- current context pressure: repeated prompts, oversized pasted logs, duplicated plans, or runaway notes
- current environment assumptions: cwd, branch, relevant service state, expected files
Minimum capture template:
## Failure Capture
- Session / task:
- Goal in progress:
- Error:
- Last successful step:
- Last failed tool / command:
- Repeated pattern seen:
- Environment assumptions to verify:Phase 2: Root-Cause Diagnosis
Match the failure to a known pattern before changing anything.
| Pattern | Likely Cause | Check |
| Maximum tool calls / repeated same command | loop or no-exit observer path | inspect the last N tool calls for repetition |
| Context overflow / degraded reasoning | unbounded notes, repeated plans, oversized logs | inspect recent context for duplication and low-signal bulk |
ECONNREFUSED / timeout | service unavailable or wrong port | verify service health, URL, and port assumptions |
429 / quota exhaustion | retry storm or missing backoff | count repeated calls and inspect retry spacing |
| file missing after write / stale diff | race, wrong cwd, or branch drift | re-check path, cwd, git status, and actual file existence |
| tests still failing after “fix” | wrong hypothesis | isolate the exact failing test and re-derive the bug |
Diagnosis questions:
- is this a logic failure, state failure, environment failure, or policy failure?
- did the agent lose the real objective and start optimizing the wrong subtask?
- is the failure deterministic or transient?
- what is the smallest reversible action that would validate the diagnosis?
Phase 3: Contained Recovery
Recover with the smallest action that changes the diagnosis surface.
Safe recovery actions:
- stop repeated retries and restate the hypothesis
- trim low-signal context and keep only the active goal, blockers, and evidence
- re-check the actual filesystem / branch / process state
- narrow the task to one failing command, one file, or one test
- switch from speculative reasoning to direct observation
- escalate to a human when the failure is high-risk or externally blocked
Do not claim unsupported auto-healing actions like “reset agent state” or “update harness config” unless you are actually doing them through real tools in the current environment.
Contained recovery checklist:
## Recovery Action
- Diagnosis chosen:
- Smallest action taken:
- Why this is safe:
- What evidence would prove the fix worked:Phase 4: Introspection Report
End with a report that makes the recovery legible to the next agent or human.
## Agent Self-Debug Report
- Session / task:
- Failure:
- Root cause:
- Recovery action:
- Result: success | partial | blocked
- Token / time burn risk:
- Follow-up needed:
- Preventive change to encode later:Recovery Heuristics
Prefer these interventions in order:
- Restate the real objective in one sentence.
- Verify the world state instead of trusting memory.
- Shrink the failing scope.
- Run one discriminating check.
- Only then retry.
Bad pattern:
- retrying the same action three times with slightly different wording
Good pattern:
- capture failure
- classify the pattern
- run one direct check
- change the plan only if the check supports it
Integration with ECC
- Use
verification-loopafter recovery if code was changed. - Use
continuous-learning-v2when the failure pattern is worth turning into an instinct or later skill. - Use
councilwhen the issue is not technical failure but decision ambiguity. - Use
workspace-surface-auditif the failure came from conflicting local state or repo drift.
Output Standard
When this skill is active, do not end with “I fixed it” alone.
Always provide:
- the failure pattern
- the root-cause hypothesis
- the recovery action
- the evidence that the situation is now better or still blocked
Other versions21 projects use a version of this.
Word for word · 4
- VoVuongThanhDat/agentflow★ 8100% same
- ArogyaReddy/https-github.com-affaan-m-everything-claude-code1★ 2100% same
- pcoulbourne/everything-claude-code★ 1100% same
- davidanthonyn/claude-skills★ 0100% same
Edited · 17
- affaan-m/ECC★ 246k97% same
- mturac/everything-openai-codex★ 8999% same
- Fmarzochi/EGC★ 6198% same
- ysyecust/everything-claude-code★ 4499% same
- gongyijie85/dsh-ecc★ 897% same
- yevhens-hue/claude-skills-starter-kit★ 415% same
- bjornjee/skills★ 286% same
- JameMy0001/antigravity-skills-hub★ 189% same
- niloykumarbarman/EVERYTHING-CLAUDE-CODE★ 1100% same
- NISTALTALSON/essential-claude-skills★ 197% same
7 more not listed
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
Used alongsideProjects with it also use brand-discovery, competitive-platform-analysis and 2 more.
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Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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Use when scoping a competitive landscape — identifying, categorising, and score-filtering a competitor set before any benchmarking begins. Decides who counts as a competitor, which tier they belong to, and which sources to mine. First step in the three-skill competitive pipeline; precedes benchmark-methodology.
Also in 3 projects that use this
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- The first 60 shown.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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